Evaluation Criteria Based on Mutual Information for Classifications Including Rejected Class  

作　　者：HU Bao-Gang WANG Yong 

机构地区：[1]National Laboratory of Pattern Recognition /Sino-French Laboratory in Computer Science, Automation and Applied Mathematics, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China [2]Graduate University of Chinese Academy of Sciences, Beijing 100049, P. Ft. China

出　　处：《自动化学报》2008年第11期1396-1403,共8页Acta Automatica Sinica

基　　金：Supported by National Natural Science Foundation of China （60275075, 60121302）

摘　　要：与用表演措施的常规评估标准不同，信息理论基于在场的标准在机器学习的应用的一个唯一的有益的特征。然而，我们仍然远非正在拥有熵类型标准的深入的理解，说，在与常规基于表演的标准的关系。这份报纸学习通用分类问题，它包括一拒绝，或未知，班。我们在场基本公式和分类基于信息学习的图解的图理论。一个靠近形式的方程为通用分类问题在规范的相互的信息和扩充混乱矩阵之间被导出。敏感方程的三个定理和定理集合为学习在相互的信息和常规表演索引之间的关系被给。我们也与常规标准比较举与相互的信息标准的优点和限制有关的数字例子和几讨论。Different from the conventional evaluation criteria using performance measures, information theory based criteria present a unique beneficial feature in applications of machine learning. However, we are still far from possessing an in-depth understanding of the ＂entropy＂ type criteria, say, in relation to the conventional performance-based criteria. This paper studies generic classification problems, which include a rejected, or unknown, class. We present the basic formulas and schematic diagram of classification learning based on information theory. A closed-form equation is derived between the normalized mutual information and the augmented confusion matrix for the generic classification problems. Three theorems and one set of sensitivity equations are given for studying the relations between mutual information and conventional performance indices. We also present numerical examples and several discussions related to advantages and limitations of mutual information criteria in comparison with the conventional criteria.

关 键 词：评价标准 信息分类 自动化技术 熵值 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]